Smart cities, smart energy solutions – thanks to the IoT

By Anne-Lindsay Beall, SAS Insights Editor

Four years ago, Envision America launched its first energy initiative: a partnership with the city of Charlotte, Duke Energy, UNC Charlotte and local businesses to help downtown Charlotte’s biggest buildings reduce energy consumption by 20 percent.

It was a lofty goal, but 61 of Charlotte’s 64 downtown high-rises signed on. And with the help of shadow meters and public kiosks that illustrated energy costs and consumption levels, building managers, tenants and occupants worked together to make it happen. They turned off lights, unplugged monitors, adjusted thermostats, revised janitorial practices and more.

The program was so successful that the White House asked Envision America to be part of its Smart Cities Initiative. The nonprofit recently held a boot camp with 10 other cities, including New York City, Los Angeles, San Diego and Dallas, to facilitate sessions on their smart city projects.

Matt Croucher, Director of Demand Side Analytics at CPS Energy

Analytics at the core of CPS Energy’s success

Meanwhile, CPS Energy of San Antonio, the largest city-owned utility in the nation, is serving more than 1.1 million customers over a 1,515 square mile service area. The utility is No. 1 in Texas and No. 7 nationally for solar generation capacity. It’s also No. 1 in Texas for demand response, and its customers’ energy bills rank among the lowest nationwide.

How does CPS Energy do it? Using a flexible architecture, including Hadoop for storage and SAS® for
big data analytics, CPS Energy finds nuggets of value in the data. “And,
among other things, it helps us deploy the products and services that
our customers want,” says Croucher.

“Analytics needs to become a core competency for us,” says Matthew Croucher, CPS Energy’s Director of Demand-Side Analytics. “With smart meters, connected devices and distributed energy resources, we can collect so much more data now.”

Croucher and Aussieker recently participated in a SAS Global Forum executive panel with moderator Tim Fairchild, Business Director of SAS’ Energy Practice, to answer questions about their strategies, challenges and successes. Here are some highlights from that discussion:

Fairchild: How important is data strategy when contemplating smart city strategy?

Aussieker: Imperative. It can help you make decisions before you implement big changes. Data can tell you what you need to do before you do it, and you’ll have more success. Gather as much data as possible, look for the low-hanging fruit and start there. It’s all about the data.

What role does analytics play?

Aussieker: It’s multilevel. When we were looking at our buildings, Duke Energy used analytics to understand building processes better. The city managers were looking at smart meter data, with leak detection layering in. We used analytics to drive all of our processes forward.

Matt, you work in a conservative industry. What makes the Internet of Things (IoT) worth taking the risk?

Croucher: Because data from the IoT can help us improve our customer service. How do we continue to deliver reliable, safe services while becoming even more customer-centric? Harnessing IoT data will help us in both the traditional blocking and tackling work, as well as enabling us to mass-segment and mass-personalize our interactions with our customers.

What are the biggest hurdles to achieving energy efficiency?

Croucher: I’m an economist so this fascinates me. Customers don’t always adopt what utilities think are the obvious solutions. How do you get over the rate-of-return obstacle inherent in many energy efficiency programs? In utilities, we have to figure out the best products and services we can offer.

Take lighting -- for many years it was the low hanging fruit, but now we need to pivot to more unique products and services to continue to offer customers savings opportunities. Time and time again, the customer research suggests that while saving money is important for customers, what customers are all focused on is the ability to control their usage and comfort. It’s about more than just rebates. How do we optimally deploy our programs so both the customer and the utility see value, and what services do customers want now and in the next 10-20 years? Those are the questions we have to answer.

Aussieker: One of the biggest hurdles in energy efficiency is a crowded field of many different solutions. When a property manager has a tenant screaming at him about the carpet or wallpaper, figuring out the best product for energy efficiency is a challenge.

We’ve taken UNC Charlotte students and professors to look at buildings and make assessments. For example, maybe the property manager needs controls for lighting when there’s one guy working late, so then the property manager needs to look at proposals for lighting. That’s a complicated process. The big companies have experts who can manage this, but smaller companies and buildings don’t have the resources.

How can analytics, smart grid, energy efficiency and demand response all fit together?

Croucher: They’re connected because they all come from consumer choice and are about consumer control. If we tell customers, “You can’t do this because we can’t manage it,” we’ll lose out on being their energy advisor. With smart meters, most utilities rushed to ensure this information was presented to its customers, but so what? If you don’t give customers the tools and options to control their usage, they won’t be interested.

Can you give me some examples of how data-rich utility companies can use smart grid to complement and facilitate a city becoming smart?

Croucher: Use analytics to figure out how big of a system you need to build, and make sure you’re asking the right questions: Do we need to oversize the system? Do we need to re-think sizing parameters, especially in a declining usage environment? Can we deploy programs to particularly troublesome areas? What approach is cost-efficient?

And don’t view “smart” as just making things cheaper or using less water or electricity: For example, how can you alert people that there’s an accident around the corner using street-lighting? The smart city concept is so much more than just efficiency in consumption. It’s about information; how can we quickly transfer the correct information to citizens so that they can help make smart decisions. That will be how you make the city more efficient.

What is the value of the culture of analytics and the smart city story?

Aussieker: How do you translate all this down to the citizen? Baby steps. Smart meters, allowing choices about saving costs, saving the environment – there are many messages to many people. Ultimately people want to live in safe, clean happy neighborhoods, and smart technology needs to help achieve this. Data and analytics can help get us there, but sometimes the translation is hard.

In Charlotte, we translated everything into economic development: Smart city makes it easier for businesses to do business in Charlotte. For example, if Charlotte charges less for utilities, you’re more likely to move your business to Charlotte.

What are your thoughts on data scientists? Are there enough out there to meet growing demand?

Croucher: Universities are focused on teaching technical skills, and aren’t teaching the business side. Data scientists in utilities will have to get used to data not being pristine, and they will spend a lot of time cleaning the data and understanding what the data represents. The first thing analytics professors should give students is a dirty data set to work with and then ask them to prove why big data analytics is worth the investment in data collection and analysis.

Aussieker: Charlotte has an open data portal, and we have lots of data scientists working for the city, but they need a translator. If you put them with the assistant city managers, there’s a gap in understanding. For smart city initiatives to succeed, there needs to be a person in between to understand what we can get out of analytics and explain that to the city program leaders to help them understand the opportunities.

Croucher: Data scientists need to be paired up with business units. And business units need to know a little about analytics to converse.